Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require training and tuning for specific tasks. A more generic and efficient solution would be an AI model that could address many tasks and support free-form dialogue in the broad field of chemistry. In its utmost form, such a generalist AI chemist could be referred to as Chemical General Intelligence. Large language models (LLMs) have recently logged tremendous success in the general domain of natural language processing, showing emerging task generalization and free-form dialogue capabilities. However, domain knowledge of chemistry is largely missing when training general-domain LLMs. The lack of such knowledge greatly hinders the performance of generalist LLMs in the field of chemistry. To this end, we develop ChemDFM, a pioneering LLM for chemistry trained on 34B tokens from chemical literature and textbooks, and fine-tuned using 2.7M instructions. As a result, it can understand and reason with chemical knowledge in free-form dialogue. Quantitative evaluations show that ChemDFM significantly surpasses most representative open-source LLMs. It outperforms GPT-4 on a great portion of chemical tasks, despite the substantial size difference. We have open-sourced the inference codes, evaluation datasets, and model weights of ChemDFM on Huggingface (https://huggingface.co/OpenDFM/ChemDFM-13B-v1.0).

翻译：人工智能（AI）在化学研究中扮演着日益重要的角色。然而，当前化学领域使用的大多数模型均为专用模型，需要针对特定任务进行训练和调优。一种更通用且高效的解决方案是构建能够处理多种任务、并支持在广泛化学领域进行自由对话的AI模型。在理想形态下，此类通用型AI化学家可被称为"化学通用智能"。近年来，大语言模型（LLMs）在自然语言处理的通用领域取得了巨大成功，展现出新兴的任务泛化能力和自由对话特性。然而，通用领域LLMs的训练过程中严重缺乏化学领域的专业知识。这种知识缺失极大阻碍了通用LLMs在化学领域的表现。为此，我们开发了ChemDFM——一个开创性的化学领域大语言模型，该模型基于340亿个来自化学文献和教科书的标记进行训练，并通过270万条指令进行微调。因此，它能够在自由对话中理解和推理化学知识。定量评估表明，ChemDFM显著超越了大多数具有代表性的开源LLMs。尽管存在显著的规模差异，但它在大部分化学任务上的表现优于GPT-4。我们已在Huggingface平台开源ChemDFM的推理代码、评估数据集和模型权重（https://huggingface.co/OpenDFM/ChemDFM-13B-v1.0）。